AI agents are moving from experiments into real business workflows. They can search internal data, open tickets, write code, call APIs and take actions on behalf of employees. That makes them more than another application to secure: each agent is also an identity with access, permissions and a trail of activity.

That shift matters now. Microsoft’s 2026 Digital Defense Report describes faster attack timelines and the early automation of parts of the attack chain. ENISA’s 2026 Threat Landscape highlights how technology dependencies widen the exposure organizations need to manage. Agents sit at the intersection: they depend on models, connectors, data sources and credentials, while acting inside trusted systems.

Treat every agent as a non-human identity

An agent that can read a mailbox, retrieve customer records or change cloud resources should have a named owner, a defined purpose and its own identity. Avoid sharing a human user’s broad credentials with an agent. Give it a separate account or workload identity, then grant only the specific data and actions it needs.

Five controls to put in place

• Inventory agents and connectors. Record who owns each agent, which model and tools it uses, what data it can reach, and where it runs. Include prototypes and agents created inside SaaS platforms.
• Limit permissions and lifetime. Use least privilege, short-lived credentials where supported, and separate identities for development and production. Review access when the agent’s task changes.
• Require approval for consequential actions. Put a human checkpoint before payments, account changes, bulk exports, production deployments or other actions that are hard to reverse.
• Log the full action chain. Capture the requesting user, agent identity, tool or connector invoked, data accessed, decision and resulting action. Keep these events searchable alongside identity and cloud logs.
• Test the boundaries. Check whether an agent can be steered by malicious instructions in documents, emails or web pages, and whether it can leak sensitive data through tools. Re-test after model, prompt, connector or permission changes.

Credential protection deserves special attention. Agents often rely on tokens that bridge identity providers, cloud services and APIs. CISA and NIST’s September 2026 guidance on protecting tokens and assertions is a useful reference for teams reviewing those trust paths.

Make agent activity visible to the SOC

Security teams should be able to answer a basic question quickly: which agent used which identity to access what, and what happened next? Send agent and connector events to your central logging platform, normalize identities, and alert on unexpected privilege changes, unusual data access, new integrations and actions outside the agent’s normal task.

Start with one high-value workflow. Map its data, tools and permissions; establish an owner; add approval gates and logging; then test misuse scenarios before expanding. This gives teams a practical way to benefit from automation while keeping access accountable and incidents investigable.

AI agents can act at machine speed. Your identity controls, monitoring and response process need to keep up.